Abstract

For securing the network, intrusion detection systems are frequently used in wireless sensor networks to fight against insider attacks by adopting the appropriate trust-based methods. Still, the sensors could create an enormous amount of data that reduces the efficacy of trust computation in the big data era. This paper aims to introduce a new attack detection system under the big data perspective. Originally, the input data is fed the preprocessing phase, in which the normalization process takes place. Further, the MapReduce framework is used to handle the bulk data by reducing it. The preprocessed data is subjected to extract the features, where it extracts the raw features, statistical features, and higher-order statistical features. Here, the feature selection is done by chi-square ranking and info-gain ranking. Further, these features are provided as the input to the classification phase, where multilayer perception (MLP) is used for detecting the presence of an attack. For making the classification more precise, weights of MLP are optimally tuned by proposed slap swarm updated sparrow optimized algorithm which integrated the concept of sparrow search algorithm and salp swarm algorithm. Finally, the performance of presented scheme is calculated to existing approaches under different metrics.

摘要

为了保护网络安全，入侵检测系统经常用于无线传感器网络，通过采用适当的基于信任的方法来对抗内部攻击。尽管如此，传感器可能会产生大量数据，从而降低大数据时代信任计算的效率。本文旨在介绍一种大数据视角下的新型攻击检测系统。最初，输入数据被送入预处理阶段，在该阶段进行标准化过程。此外，MapReduce框架用于通过减少数据来处理海量数据。对预处理后的数据进行特征提取，提取原始特征、统计特征和高阶统计特征。在这里，特征选择是通过卡方排名和信息增益排名来完成的。更远，这些特征作为分类阶段的输入提供，其中多层感知（MLP）用于检测攻击的存在。为了使分类更加精确，综合了麻雀搜索算法和樽海鞘群算法的概念，提出了slap swarm更新麻雀优化算法，对MLP的权重进行优化调整。最后，在不同指标下，根据现有方法计算所提出方案的性能。